PREDICT: Survival prediction in patients with breast cancer - Evidencio
PREDICT: Survival prediction in patients with breast cancer
Current version is to assess the integration possibilities using the URL
PREDICT is designed to calculate estimates of survival with and without adjuvant treatment to show the predicted benefit of providing specific treatment options. 
Autorzy badań: Wishart GC, Azzato EM, Greenberg DC, Rashbass J, Kearins O, Lawrence G, Caldas C, Pharoah PD
Wersja: 1.7
  • Publiczny
  • Onkologia
  • {{ modelType }}
  • Algorithm
  • Szczegóły
  • Walidacja
  • Zapisywanie danych wejściowych
  • Wejście obciążenia
  • Języki
    English Dutch

  • {{ toolbarErrorMessage }}

Wyświetlacz
Jednostki

{{ section.title }}

{{ section.description }}

Obliczanie wyniku

Ustaw więcej parametrów, aby wykonać obliczenia

Predicted survival is: %

{{ resultSubheader }}
{{ $t('download_result_availability') }}
{{ chart.title }}
Interwał wyników {{ additionalResult.min }} do {{ additionalResult.max }}

Informacje warunkowe

Twoje wprowadzone teksty zostały zmienione. Zaktualizuj wynik. Aktualizuj

How this model can be applied: 
Online prognostic tools such as PREDICT are increasingly used by oncologists in clinical practice to inform patients and support treatment decisions regarding adjuvant, systemic therapy. Validation studies have shown that PREDICT generally provides reasonable to good estimates for overall 5- and 10-year mortality in patients with breast cancer.1-3

Limitations:
Prognostic tools such as PREDICT should be used with caution because of intrinsic variations in outcomes obtained and because the threshold to discuss adjuvant, systemic treatment is low. In a number of subgroups, PREDICT shows under- and overestimates, according to a study published in 2017 by Ellen G. Engelhardt (LUMC) and a group of colleagues from home and abroad.4 

Scientific Support: 
Several validation studies have been conducted internationally on the performance of PREDICT (see 'validations' tab at www.evidencio.com). In June 2017, a study on the prognostic accuracy of PREDICT was published by Ellen G. Engelhardt and colleagues.4 The researchers collected a consecutive series of 2,710 patients with breast cancer aged 50 years or younger diagnosed between 1990 and 2000. C-statistics were used to estimate calibration accuracy and discriminant accuracy for overall 10-year mortality and breast cancer-specific mortality. 

Overall, PREDICT's calibration proved good (predicted versus observed overall mortality). However, PREDICT does tend to underestimate overall mortality (regardless of cause of death) in subgroups with good prognosis (degree of underestimation: -2.9% to -4.8%) and overestimate it in subgroups with poor prognosis (degree of overestimation: 2.6% to 9.4%). In patients up to 35 years of age, PREDICT underestimated overall mortality by 6.6%. Breast cancer-specific mortality was overestimated by PREDICT by 3.2%. The researchers also observed an apparent overestimation of breast cancer-specific mortality in various subgroups (range 3.2% to 14.1%). 

References:

  1. Wishart GC, Azzato EM, Greenberg DC, et al. PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer. Breast Cancer Res. 2010;12(1):R1.
  2. Wishart GC, Bajdik CD, Dicks E, et al. PREDICT Plus: development and validation of a prognostic model for early breast cancer that includes HER2. Br. J. Cancer 2012;107(5):800-7.
  3. Wishart GC, Rakha E, Green A, et al. Inclusion of KI67 significantly improves performance of the PREDICT prognostication and prediction model for early breast cancer. BMC Cancer. 2014;14:908.
  4. Engelhardt EG, van den Broek AJ, Linn SC, et al. Accuracy of the online prognostication tools PREDICT and Adjuvant! for early-stage breast cancer patients younger than 50 years. Eur J Cancer. 2017;78:37-44.

{{ file.classification }}
PRO
Uwaga
Notatki są widoczne tylko w pobranym wyniku i nie zostaną zapisane przez Evidencio

Niniejszy algorytm służy celom edukacyjnym, szkoleniowym i informacyjnym. Nie może być wykorzystywany do wspierania podejmowania decyzji medycznych ani do świadczenia usług medycznych lub diagnostycznych. Przeczytaj naszą pełną wersję disclaimer.

Algorytmy leżące u podstaw Część
Komentarze
Komentarz
Wprowadź komentarz
Komentarze są widoczne dla każdego

Algorytm informacja zwrotna

Brak opinii 1 Komentarz {{ model.comments.length }} Komentarze
Na {{ comment.created_at }} {{ comment.user.username }} niezarejestrowany już autor napisał:
{{ comment.content }}
logo

Zaloguj się, aby włączyć funkcje drukowania Evidencio

Aby korzystać z funkcji drukowania Evidencio, musisz być zalogowany.
Jeśli nie masz konta społecznościowego Evidencio, możesz utworzyć bezpłatne konto osobiste pod adresem:

https://www.evidencio.com/registration

Wydrukowane wyniki - przykłady {{ new Date().toLocaleString() }}

Korzyści z konta społecznościowego Evidencio

With an Evidencio Community account you can:

  • Create and publish your own prediction algorithms.
  • Share your prediction algorithms with your colleagues, research group, organization or the world.
  • Review and provide feedback on algorithms that have been shared with you.
  • Validate your algorithms and validate algorithms from other users.
  • Find algorithms based on Title, Keyword, Author, Institute, or MeSH classification.
  • Use and save prediction algorithms and their data.
  • Use patient specific protocols and guidelines based on sequential algorithms and decision trees.
  • Stay up-to-date with new algorithms in your field as they are published.
  • Create your own lists of favorite algorithms and topics.

A personal Evidencio account is free, with no strings attached!
Join us and help create clarity, transparency, and efficiency in the creation, validation, and use of medical prediction algorithms.

Zastrzeżenie: Same obliczenia nigdy nie powinny dyktować opieki nad pacjentem i nie zastępują profesjonalnego osądu.
Terms & Conditions Privacy Policy Disclaimer Contact Refuse cookies
Evidencio v3.40 © 2015 - 2026 Evidencio. All Rights Reserved